Can the RTX 5090 32 GB run Wan 2.2 T2V A14B?
Yes. FP8 fits on the RTX 5090 32 GB with 13.4 GB to spare.
FP8 (14.3 GB) needs about 18.6 GB in use, so 13.4 GB of the 32 GB stays free: room for a larger image, a LoRA or a ControlNet.
01What to download
| Part | File | Folder | Size | |
|---|---|---|---|---|
| Model | wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors high-noise model (early steps) FP8 · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 14.3 GB | Download → |
| Model | wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors low-noise model (late steps) FP8 · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/diffusion_models | 14.3 GB | Download → |
| Text encoder | umt5_xxl_fp8_e4m3fn_scaled.safetensors UMT5-XXL FP8 (scaled) · Comfy-Org/Wan_2.1_ComfyUI_repackaged | models/text_encoders | 6.7 GB | Download → |
| VAE | wan_2.1_vae.safetensors Wan 2.1 VAE · Comfy-Org/Wan_2.2_ComfyUI_Repackaged | models/vae | 0.3 GB | Download → |
| Total download · keep about the same free on disk | 35.6 GB | |||
Alternatives: UMT5-XXL FP16 (11.4 GB). Sizes from Hugging Face, 2026-10-01.
Free ComfyUI workflow, tested on my RTX 5060 Ti: FP8 workflow (.json) · GGUF Q5_K_M workflow (.json). Drag the file into ComfyUI. All workflows →
System RAM: 48 GB or more. These files (both noise models) take about 35.6 GB of RAM while loaded, 41.6 GB with Windows and ComfyUI. What happens with less. calculated
02Every Wan 2.2 T2V file on 32 GB
| File | Size | Needed | On this card | Quality | Download |
|---|---|---|---|---|---|
| 16-bit SAFETENSORS · Comfy-Org | 28.6 GB | 32.9 GB | spills 0.9 GB | the original weights | Hugging Face → |
| FP8 ← SAFETENSORS · Comfy-Org | 14.3 GB | 18.6 GB | fits · 13.4 GB spare | practically identical to the original | Hugging Face → |
| Q8_0 GGUF · QuantStack | 15.4 GB | 19.7 GB | fits · 12.3 GB spare | practically identical to the original | Hugging Face → |
| Q6_K GGUF · QuantStack | 12.0 GB | 16.3 GB | fits · 15.7 GB spare | very close to the original | Hugging Face → |
| Q5_K_M GGUF · QuantStack | 10.8 GB | 15.1 GB | fits · 16.9 GB spare | close; small differences in fine detail | Hugging Face → |
| Q4_K_M GGUF · QuantStack | 9.7 GB | 14.0 GB | fits · 18.0 GB spare | good; some loss in fine detail and text | Hugging Face → |
| Q3_K_M GGUF · QuantStack | 7.2 GB | 11.5 GB | fits · 20.5 GB spare | noticeable loss of detail | Hugging Face → |
| Q2_K GGUF · QuantStack | 5.3 GB | 9.6 GB | fits · 22.4 GB spare | heavy loss; a last resort | Hugging Face → |
“Needed” adds 3.5 GB of working memory for Wan 2.2 T2V and a 0.8 GB reserve. Sizes 2026-10-01. Wan 2.2 A14B uses two files of this size (high-noise and low-noise); only one sits in VRAM at a time, both must fit in system RAM.
03Measured and reported results
Nobody has sent measured numbers for this pair yet. If you run Wan 2.2 T2V on a RTX 5090 32 GB, send your time per image and peak VRAM and it will appear here, credited.
04About the RTX 5090 32 GB
The RTX 5090 32 GB is a Blackwell GPU with FP8 and FP4 hardware: ComfyUI computes Comfy-Org's FP8 files natively here, and NVFP4 files (where a model offers them) are faster still.
32 GB GDDR7 · 512-bit · 1792 GB/s · Blackwell. Everything that runs on it →
05Questions
How much VRAM does Wan 2.2 T2V need?
Around 9.6 GB with the smallest file (Q2_K) and 18.6 GB with an 8-bit file (FP8). The full 16-bit file needs about 32.9 GB.
Which Wan 2.2 T2V file should I download for the RTX 5090 32 GB?
FP8 (14.3 GB) from Comfy-Org/Wan_2.2_ComfyUI_Repackaged.
FP8 or Q8_0 for Wan 2.2 T2V on the RTX 5090 32 GB?
FP8 (14.3 GB): the RTX 5090 32 GB computes it natively, while Q8_0 (15.4 GB) is unpacked on the fly.
How much do I need to download for Wan 2.2 T2V on the RTX 5090 32 GB?
35.6 GB: wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors, wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors, umt5_xxl_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors.